Grok 4.1 Fast vs Qwen3 235B-A22B Thinking 2507 vs GLM-4.6V-Flash
Grok 4.1 Fast comes out ahead, 66 to 63 and 42 on our weighted score, though GLM-4.6V-Flash is 5% cheaper per token.
- Our pick
xAIGrok 4.1 Fast
66/100- ECI—
- Price$0.20 / $0.50
- Context2M
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
Z.ai (Zhipu)GLM-4.6V-Flash
63/100- ECI—
- Price$0.161 / $0.559
- Context128K
Grok 4.1 Fast is our pick
Grok 4.1 Fast is the better all-round choice, scoring 66/100 against GLM-4.6V-Flash (63) and Qwen3 235B-A22B Thinking 2507 (42). It leads on context window. GLM-4.6V-Flash wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGLM-4.6V-FlashGLM-4.6V-Flash $0.261 · Grok 4.1 Fast $0.275 · Qwen3 235B-A22B Thinking 2507 $0.95 per 1M tokens (3:1 blend)
- Longest contextGrok 4.1 FastGrok 4.1 Fast 2,000,000 · Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.6V-Flash 128,000 tokens
- Widest inputsGLM-4.6V-FlashGrok 4.1 Fast: Text, Images · Qwen3 235B-A22B Thinking 2507: Text · GLM-4.6V-Flash: Text, Images, Video
- Self-hostingQwen3 235B-A22B Thinking 2507 and GLM-4.6V-FlashPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Grok 4.1 Fast | Qwen3 235B-A22B Thinking 2507 | GLM-4.6V-Flash |
|---|---|---|---|---|
| Price | 50% | 73 | 49 | 74 |
| Inputs & features | 30% | 50 | 35 | 70 |
| Context window | 20% | 72 | 37 | 24 |
| Overall | 100% | 66/100 | 42/100 | 63/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Grok 4.1 FastxAI | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) | GLM-4.6V-FlashZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 143.8 | — |
| ECI rank | — | #93 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 80.1% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 86.7% | — |
| SimpleQA VerifiedShort factual questions | — | 40.4% | — |
| Price per million tokens | |||
| Input | $0.20 | $0.30 | $0.161 (best) |
| Output | $0.50 (best) | $2.90 | $0.559 |
| Cached input | — | — | — |
| Blended (3:1) | $0.275 | $0.95 | $0.261 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 6 providers | Median of 11 providers | Median of 2 providers |
| Limits | |||
| Context window | 2,000,000 tokens (best) | 262,144 tokens | 128,000 tokens |
| Max output | 30,000 tokens | 81,920 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | OpenApache 2.0 | Open |
| API model ID | — | — | glm-4.6v-flash |
| API providers | 8 | 12 (best) | 6 |
| Released | Nov 19, 2025 | Jul 25, 2025 | Dec 8, 2025 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Grok 4.1 Fast$3.00
- Qwen3 235B-A22B Thinking 2507$8.80
- GLM-4.6V-Flash$2.73
Which should you choose?
Which is better: Grok 4.1 Fast, Qwen3 235B-A22B Thinking 2507 or GLM-4.6V-Flash?
Grok 4.1 Fast is the better all-round choice, scoring 66/100 against GLM-4.6V-Flash (63) and Qwen3 235B-A22B Thinking 2507 (42). It leads on context window. GLM-4.6V-Flash wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Grok 4.1 Fast, Qwen3 235B-A22B Thinking 2507 or GLM-4.6V-Flash?
GLM-4.6V-Flash is cheaper at $0.161 input / $0.559 output per million tokens (median across 2 API providers; free on Z.AI). Grok 4.1 Fast costs $0.20 input / $0.50 output per million tokens (median across 6 API providers); Qwen3 235B-A22B Thinking 2507 costs $0.30 input / $2.90 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.261 per million tokens for GLM-4.6V-Flash versus $0.275 for Grok 4.1 Fast (1.1× as much) and $0.95 for Qwen3 235B-A22B Thinking 2507 (3.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Grok 4.1 Fast has not been scored yet, Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8 and GLM-4.6V-Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Grok 4.1 Fast, Qwen3 235B-A22B Thinking 2507 and GLM-4.6V-Flash yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Grok 4.1 Fast has the largest context window at 2,000,000 tokens, against 262,144 for Qwen3 235B-A22B Thinking 2507 and 128,000 for GLM-4.6V-Flash. Maximum output per response: Grok 4.1 Fast up to 30,000, Qwen3 235B-A22B Thinking 2507 up to 81,920, GLM-4.6V-Flash up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Grok 4.1 Fast accepts text and images; Qwen3 235B-A22B Thinking 2507 accepts text; GLM-4.6V-Flash accepts text, images and video. GLM-4.6V-Flash handles the widest range of inputs.
Are any of these open source?
Qwen3 235B-A22B Thinking 2507 and GLM-4.6V-Flash publishes its weights (Apache 2.0) and can be self-hosted; Grok 4.1 Fast is proprietary.
Which is newer?
GLM-4.6V-Flash is the newest, released Dec 8, 2025. Grok 4.1 Fast came out Nov 19, 2025; Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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